Top Senior Data Engineer Jobs in Atlanta, GA

Designs and maintains production data pipelines, models claims and operational data, and improves reliability through testing, monitoring, and alerting. Owns workflow troubleshooting, backfills, performance optimization, documentation, access controls, and CI/CD practices. Partners with business and technical teams to define requirements and deliver trusted datasets using SQL, Python, cloud data warehouses, and orchestration tools.

Designs and optimizes scalable Azure Databricks data pipelines, Lakehouse architectures, Delta Lake layers, data models, and distributed Spark processing. Partners with analytics and data science teams to deliver reliable datasets for AI initiatives. Develops autonomous agents using Mosaic AI, LangChain, or AutoGen; builds vector search indexes; and implements retrieval, orchestration, evaluation, monitoring, alerting, observability, and governance capabilities.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

Designs trusted Snowflake data products and enterprise semantic models that standardize business concepts, metrics, relationships, and rules. Builds Cortex semantic views and agents, validates AI-generated results, develops regression tests, and maintains metadata, lineage, and documentation. Leads modeling initiatives from discovery through production while partnering with business and technical stakeholders to improve analytics, automation, and AI consistency.

Build and maintain scalable batch, streaming, and real-time data pipelines, platforms, data lakes, warehouses, and lakehouses. Implement data quality, governance, security, observability, automation, and cloud modernization. Partner with product, engineering, analytics, data science, and business teams to deliver analytics, AI/ML, reporting, and customer insight solutions. Troubleshoot production issues, guide architecture, mentor engineers, and provide technical leadership across marketing data initiatives.

Designs, builds, and maintains complex data systems, including scalable data infrastructure, batch and streaming pipelines, data models, data products, and cloud-based architectures. Develops automated deployment pipelines, improves data systems, and supports data analysis and advanced analytics. Collaborates with cross-functional teams to gather requirements and ensure robust, governed, and accessible data solutions.

Leads the design, implementation, and operation of scalable production data pipelines and architectures across cloud platforms. Defines technical approaches and success metrics with stakeholders, monitors reliability, mentors junior engineers, collaborates with data scientists and analysts, and develops reusable data engineering practices. The role requires production pipeline experience across multiple cloud providers, comfort in startup environments, and knowledge of regulated-industry compliance, security, and governance requirements.

Leads large-scale data engineering initiatives, overseeing end-to-end delivery and mentoring junior engineers. Designs scalable, secure data architectures; automates data ingestion, transformation, and deployment; monitors pipelines; and resolves technical issues. The role requires advanced distributed data processing, data modeling, data warehousing, cloud platforms, analytics tools, CI/CD, DevOps, SQL, Python, and data pipeline technologies.

Manage and support PostgreSQL and MySQL databases in a large, high-transaction environment. Own incidents and service requests, meet SLAs, review backups and monitoring reports, respond to alerts, troubleshoot performance issues, and maintain high availability, partitioning, security, and recovery processes. Improve database provisioning automation, monitoring, and alerting while collaborating with engineering teams on recurring issues. The role is hybrid in the Atlanta area.

Lead the design and delivery of scalable batch and real-time data pipelines across AWS, Snowflake, PostgreSQL, MongoDB, dbt, and Kafka. Establish standards for data modeling, governance, security, testing, observability, and reliability. Build trusted datasets and semantic layers for BI, analytics, AI/ML, RAG, and LLM applications. Drive platform optimization, data integration, MDM, technical strategy, mentoring, and cross-functional collaboration.

Build and maintain enterprise ETL/ELT pipelines, data models, warehouses, and lakehouse environments using Python, SQL, Databricks, and Azure. Integrate and transform large datasets, enforce data quality and governance, optimize pipeline performance, and document data architecture. Collaborate with cloud engineers, data scientists, analysts, and operational leaders to deliver scalable, secure, analysis-ready data systems.

Design, build, secure, and operate Microsoft’s clean room data platform and infrastructure. Develop scalable data onboarding, identity resolution, audience activation, measurement, and partner collaboration capabilities. Implement access controls, encryption, network isolation, monitoring, auditing, compliance, and reliability standards for sensitive data environments. Automate provisioning, deployments, and operational controls while leading incident investigations and platform improvements. Partner with engineering, privacy, security, marketing, data engineering, and external organizations on long-term privacy-safe data collaboration solutions.

Designs and maintains scalable, low-latency data pipelines for real-time and batch market data. Builds ETL/ELT processes, optimizes storage and streaming systems, supports productionizing AI and machine-learning models, and improves data quality, observability, reliability, and cost efficiency. Collaborates with data scientists, ML engineers, and product teams to evolve Fetcherr’s data platform.

Lead the architecture, development, governance, and operation of scalable data platforms, pipelines, schemas, databases, and warehousing infrastructure. Guide data acquisition, transformation, cataloging, and integration across cloud and local environments while ensuring reliability, security, performance, and data quality. Collaborate with product and engineering leadership, recommend data solutions, establish technical standards, and mentor engineering teams.

Design, build, and maintain ETL/ELT pipelines and API integrations to ingest, cleanse, transform, and analyze infrastructure and cloud data. Produce analytics, dashboards, and executive reports; automate workflows and support near-real-time processing. Follow SDLC/DevOps practices (Git, CI/CD), ensure data governance, quality, lineage, and support incident, release, and compliance activities.

Build, deploy, and operationalize complex data pipelines and solutions using Databricks, cloud platforms, Python, and SQL. Responsibilities include data transformation, cleansing, governance, security, quality, source analysis, testing, defect resolution, and support for AI, machine learning, and business intelligence. The role also involves cross-team collaboration, stakeholder education, and delivery of resilient data products.

Design, build, and optimize scalable ELT pipelines into BigQuery, create dbt models and data models, implement data quality, observability, CI/CD, and IaC practices, and partner with stakeholders to deliver trusted enterprise data products.

Design, build, and optimize modern Azure and Databricks data platforms for clients. Develop batch and streaming ingestion pipelines, lakehouse and medallion architectures, dimensional models, and transformation workflows. Lead discovery sessions, requirements workshops, architecture discussions, and solution reviews. Recommend practices for governance, lineage, monitoring, DevOps, security, and data modeling while translating business needs into scalable technical solutions and communicating with technical and executive stakeholders.

Build, optimize, and maintain data pipelines, ETL processes, data architectures, AI solutions, and analytics platforms. Develop cloud-based solutions using Azure Functions and containerization, implement CI/CD workflows, integrate databases and APIs, and ensure data quality, monitoring, reliability, and documentation. Troubleshoot production issues, collaborate with cross-functional teams, and apply modern data engineering, AI, cloud, and DevOps practices.

Lead design, build, and maintain scalable data pipelines, ETL processes, data models, and storage solutions. Support production data ecosystem, optimize performance, ensure data quality and security, integrate data sources/APIs, and provide technical guidance to teams while collaborating with stakeholders.

Build and maintain centralized pricing data infrastructure, scalable workflows, and data pipelines; integrate AI capabilities; deliver pricing tools, reporting, and governance to support data-driven pricing and budgeting across the firm.

Build, operate, and monitor ETL/ELT pipelines between marketing, sales, and operational systems into Databricks. Implement dimensional models, identity resolution, data quality checks, API integrations, and scheduled/event-driven syncs to support segmentation, attribution, and campaign activation. Maintain documentation, alerting, and runbooks.

Design, build, and operationalize complex data pipelines and products to support analytics and AI/ML. Analyze and transform source data, enforce data governance/security/quality, resolve high-complexity incidents, mentor team members, and partner with business stakeholders to deliver reusable, scalable data platform capabilities.

Leads data engineering for Payment Operations monitoring transformation. Designs and maintains Snowflake pipelines, tables, stored procedures, monitoring scenarios, KPI infrastructure, and data quality controls. Serves as the Snowflake subject matter expert, documents architecture and operational processes, guides technical partners, and translates business needs into scalable data solutions. The role requires production data engineering experience, advanced SQL, Snowflake expertise, job scheduling, pipeline operations, and stakeholder communication.

Lead design and delivery of the enterprise Data Platform including data lake, analytics, and certified data products. Architect solutions, automate pipelines, implement monitoring, mentor engineers, translate business needs into technical architectures, and document systems for sustainability.